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Alan Wilde (1929-2023), a founding member of the Journal of Modern Literature's editorial board, passed away November 25, 2023, at age 94. He served on the editorial board from the [...]

According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage. [ABSTRACT FROM AUTHOR]

Purpose: Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis is a highly heterogeneous syndrome with distinct phenotypes that impact immune function and response to infection. To develop targeted therapeutics, immunophenotyping is needed to identify distinct functional phenotypes of immune cells. In this study, we utilized our Organ-on-Chip assay to categorize sepsis patients into distinct phenotypes using patient data, neutrophil functional analysis, and proteomics. Methods: Following informed consent, neutrophils and plasma were isolated from sepsis patients in the Temple University Hospital ICU (n=45) and healthy control donors (n=7). Human lung microvascular endothelial cells (HLMVEC) were cultured in the Organ-on-Chip and treated with buffer or cytomix ((TNF/IL-1b/IFNg). Neutrophil adhesion and migration across HLMVEC in the Organ-on-Chip were used to categorize functional neutrophil phenotypes. Quantitative label-free global proteomics was performed on neutrophils to identify differentially expressed proteins. Plasma levels of sepsis biomarkers and neutrophil extracellular traps (NETs) were determined by ELISA. Results: We identified three functional phenotypes in critically ill ICU sepsis patients based on ex vivo neutrophil adhesion and migration patterns. The phenotypes were classified as: Hyperimmune characterized by enhanced neutrophil adhesion and migration, Hypoimmune that was unresponsive to stimulation, and Hybrid with increased adhesion but blunted migration. These functional phenotypes were associated with distinct proteomic signatures and differentiated sepsis patients by important clinical parameters related to disease severity. The Hyperimmune group demonstrated higher oxygen requirements, increased mechanical ventilation, and longer ICU length of stay compared to the Hypoimmune and Hybrid groups. Patients with the Hyperimmune neutrophil phenotype had significantly increased circulating neutrophils and elevated plasma levels NETs. Conclusion: Neutrophils and NETs play a critical role in vascular barrier dysfunction in sepsis and elevated NETs may be a key biomarker identifying the Hyperimmune group. Our results establish significant associations between specific neutrophil functional phenotypes and disease severity and identify important functional parameters in sepsis pathophysiology that may provide a new approach to classify sepsis patients for specific therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Pelvic venous obstruction (PVO), defined as greater than 50% stenosis or occlusion of pelvic veins, is a known risk factor for deep vein thrombosis (DVT). DVT is a known risk factor for chronic thromboembolic pulmonary hypertension (CTEPH), but the prevalence of PVO in CTEPH is unknown. Methods: This cross-sectional study at Temple University's tertiary referral center for Pulmonary Hypertension, Right Heart Failure, and CTEPH sought to identify the presence of PVO in patients with CTEPH who underwent cardiac catheterization, pulmonary angiography, and venography. Results: A total of 193 CTEPH patients were referred for pulmonary angiography, and among these, 148 underwent venography. PVO was identified in 65 (44%) patients. Lower extremity (LE) DVT was associated with PVO (p = 0.004). The severity of pulmonary hypertension was similar with and without PVO (mean pulmonary artery pressure 43.0 ± 10.3 mm Hg vs. 43.8 ± 12.4 mm Hg, p = 0.70), as was the need for pulmonary thromboendarterectomy (69.2% vs. 61.4%, p = 0.32). Conclusions: Pelvic vein obstruction is common and a novel clinical association in patients with CTEPH, particularly in patients with a history of LE DVT. PVO and its role in CTEPH warrants further study, including the potential role of revascularization to mitigate further risk. [ABSTRACT FROM AUTHOR]

In this manuscript, a deep learning method is implemented for automatic detection of abnormal and normal Electroencephalogram (EEG) signals, especially for early diagnosis of Epileptic Seizure (ES) disease. Firstly, 8th order Butter Worth Filter (BWF) is applied for removing artifacts from Bern Barcelona (BB), Bonn University (BU), and Temple University Hospital (TUH) datasets. Next, the swarm decomposition method is employed for signal decomposition where it provides a significant frequency localization by separating the essential signal components from the denoised EEG signals. Then, the semantic feature extraction is performed for extracting feature values from the decomposed signals. The feature extraction includes the benefits like reduction in overfitting risk, accuracy improvement, and speed up the training process. Further, the extracted multidimensional feature values are reduced by proposing enhanced Salp Swarm Algorithm (SSA), where it selects optimum feature values for classification. The enhanced SSA uses Whale Optimization Algorithm (WOA) for identifying the potential feature values from the multi-dimensional feature space. Lastly, the selected features are fed to the Long Short Term Memory (LSTM) network for classifying abnormality and normality of seizures. The experimental results show that the enhanced SSA based LSTM network classifies abnormality and normality of seizure with classification accuracy of 97.84%, 99.32%, and 99.20% on the BU, BB, and TUH datasets. [ABSTRACT FROM AUTHOR]

The article focuses on the collaborative effort at Temple University to create The Beggar's Opera Digital Edition, involving multidisciplinary contributions from Steven Newman, Becca Fülöp, Fred Rowland, and Alex Wermer-Colan. Topics covered include project goals, editorial challenges in music transcription, text and music encoding, web display decisions, and future plans for certification and expansion, emphasizing the collaborative and technical aspects of the digital humanities project.

Background: Gastroparesis has a substantial impact on the quality of life but has limited treatment options, which makes it a public health concern. No bibliometric studies on gastroparesis have been published thus far. Thus, this article aims to summarize and analyze research hotspots to provide a reference for clinical researchers. Materials and methods: Gastroparesis-related research articles were searched in the Web of Science Core Collection (WOSCC), and relevant information was extracted after screening. A total of 1033 documents were analyzed with the bibliometric method using Microsoft Excel, Citespace, and VOSviewer. Results: Overall, our search retrieved 1033 papers contributed by 966 research institutions from 53 countries. Since 1980, publications in this field have increased rapidly. United States (n = 645) and Temple University (n = 122) were the most productive country and institution, respectively. Parkman, with 96 publications, was the most prominent author. Conclusions: Research hotspots in gastroparesis can be summarized into four domains: innovation in diagnostic modalities, change of oral therapeutic agents, choice of surgical interventions, and pathological mechanisms. Future research on gastroparesis should focus on the quality of life of patients, diagnostic techniques, pyloromyotomy, and transpyloric stent placement. [ABSTRACT FROM AUTHOR]

Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it's side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure's ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all. [ABSTRACT FROM AUTHOR]

Tina Sloan Green, Nikki Franke, Alpha Alexander, and Linda Greene represent an integral part of a culture of Black women in sports who created a place and space for themselves and others in opposition to the long history of racism and sexism that suffused sports in the United States and global world. As founders of the Black Women in Sport Foundation (BWSF), their activism and organizing on behalf of Black women and girls in, and beyond sport, is as varied as it is vast. While the founders have been interviewed about the BWSF numerous times throughout their respective careers, those interviews fail to capture the paths that led them to successful careers or the incorporation of the BWSF. Using oral history narratives, this paper contends that their experiences from childhood to young adulthood offer incredible insights about the origins and evolution of their critical consciousness around race and gender that emerged during their formative years. It illuminates the familial, communal, educational, and sporting legacies of BWSF founders from childhood to the mid-to-late 1970s, when their worlds collided at Temple University. Their histories underscore how they navigated and negotiated the ideologies of racism and sexism from childhood to adulthood. As young Black women who lived before the passage of Title IX, their stories depict the early struggles and successes of women and girls' participation in sports and broader society. Individually and collectively, BWSF founders' oral history narratives offer a great understanding of Black women in sports and society in the past and present. [ABSTRACT FROM AUTHOR]

The struggle for civil rights has never been black and white; multiracial coalitions have been imperative in fighting for equality under the Constitution. With the creation of the new Hon. Nelson A. Diaz Professorship in Law, Temple University Beasley School of Law has brought important recognition to the role Latinxs have played in this movement. It has spotlighted a trailblazer in the Latinx community and committed to support other leaders who follow in his footsteps. The authors participated in a panel discussion in the Spring of 2023, which celebrated Judge Diaz and Latinx leadership in the law. This Essay will build on that event, detailing Judge Diaz's legacy of advocacy and service and exploring the many Latinx contributions to the civil rights movement that the Projessorship seeks to showcase. With Judge Diaz's generous gift, Temple has invested in Latinx representation and recognition, broadening our understanding of who fights for our civil rights, and for whom. [ABSTRACT FROM AUTHOR]